Carlsbad, California, April 29, 2025 – Edge-to-core AI platform GigaIO has unveiled AI training, fine-tuning, and inference benchmarks that demonstrate the performance, cost and power efficiency of GigaIO’s AI fabric compared with RDMA over Converged Ethernet (RoCE). According to the company, results include 2x faster training and fine-tuning and 83x better time to first token […]
Optical I/O: The Key to Unlocking AI Infrastructure, Profitability and Performance
October 1, 2024 by
[SPONSORED GUEST ARTICLE] Scaling GenAI inference performance requires increasing the number of GPUs or accelerators working in parallel within the scale-up domain. In-package optical I/O offers a path forward by breaking the ….
Improving AI Inference Performance with GPU Acceleration in Aerospace and Defense
August 25, 2022 by
The aerospace/defense industry often must solve mission-critical problems as they arise while also planning and designing for the rigors of future workloads. Technology advancements let aerospace/defense agencies gain the benefits of AI, but it’s essential to understand these advancements and the infrastructure requirements for AI training and inference.
Intelligent Fabrics for the Next Wave of AI Innovation
September 10, 2020 by
In this sponsored post, our friend John Spiers, Chief Strategy Officer at Liqid, discusses how resource utilization and the soaring costs surrounding it are a constant push and pull issue for IT Departments. Now with the emergence of AI and machine learning, resource utilization is far more front and center than it has ever been. Managing legacy hardware in a hyperconverged environment just as you always have is not going to cut it, because the people and hardware costs associated with these extremely heavy workloads are tremendous. Intelligent fabrics and composable infrastructure software deliver a solution to the problem that allows IT providers the ability to pool and deploy their hardware resources to adapt to the workload at hand, then re-deploy as required for a balanced system that can address the demands of AI and machine learning.
